A new writer, Lyndsay Wise, makes her debut with an article about the growing importance of data management initiatives to business professionals
In the quest for successful business intelligence, many organizations look at how they can access information through dashboards and scorecards, or interactive analysis and reporting. Self-service models and the increasing ability to interact with information more easily let more people access analytics and gain broader business insights. But this process also tends to overlook the increasing role of data management in delivering valuable, reliable, and relevant information to the business. After all, without a strong data infrastructure it is difficult to achieve confidence in the data being delivered.
Although front-end applications may provide organizations with the insights they need to identify opportunities and make better decisions, the reality is that the only way to successfully achieve this on a long-term basis is through a commitment to best practices in data management. And with an increasing focus on self-service BI and broader data discovery for business people, the reality is that BI initiative management is slowly shifting to become the responsibility of those outside traditional IT roles.
Consequently, this means that business decision makers need to have a better understanding of data management, how it works, and why it is important to overall success for both analytical and operational applications.
Why is data important – what happens without access
Historically, data was managed exclusively by IT departments. Any changes, additions, discrepancies, etc. that needed to be addressed, were done so behind the scenes. Business units submitted requests and then IT would implement the changes. With business intelligence becoming more user-driven, and with business decision makers wanting direct access to various types of data, the importance of information management has never been greater.
Having business analysts and other end users interact with and manipulate data in Excel to meet their business needs no longer works, and most likely never did. Although valid data may have been extracted from reliable data sources, once it was in the hands of an individual, its reliability and accuracy could not be guaranteed. Adding calculations, information from disparate data sources, and trying to balance discrepancies from multiple reports are just some of the ways in which spreadsheets have been used over time.
Unfortunately, the lack of security, data quality, ability to meet compliance requirements, and the like, are just some of the reasons why independent management of data does not work. Others include lack of trust in data, inability to consolidate information to get a broad view of the organization, inability to manage metrics effectively, and a lack of visibility into opportunities and challenges.
The bottom line for organizations is that in order to maintain valid, accurate, and reliable information access, data management challenges need to be addressed. Without access to the information needed to make better business decisions, companies cannot compete with the broader market landscape. With information requirements expanding, organizations cannot simply piece together their data sources randomly – they require a strong data management organization to ensure that data is transformed into valuable and actionable information. And luckily, the vendor landscape has expanded to provide data management platforms that address these challenges.
Introductory data management considerations for organizations
Although disparate industries may require different types of data to become better decision makers, there are some overarching considerations across vertical industries that all businesses need to look at in order to identify how to best manage their data.
Formalized data management initiatives are becoming an essential aspect for many organizations, with the management of data assets delegated to those within business units who understand the business rules behind the data itself.
This means that a wider variety of people within the organization require broader insights into their data. After all, data management involves more than databases, consolidated information, and master records. Business decision makers require an understanding of how business rules affect data flow and what areas of data management are essential versus “nice to have”.
In order to do so, companies should start by asking the following questions:
- What are the three most important challenges we’re currently facing? Even though data management assumes that an organization will be managing large and/or diverse data sets, it is always important to make sure that any data-oriented initiative integrates tightly with current gaps in business visibility or performance. For instance, companies may look at Customer MDM (Customer Master Data Management) as a starting point due to the fact that they cannot currently understand the value of individual customers, how to target up-selling campaigns, or why retention levels are low.
- Which stakeholders should be involved in the process? Taking the Customer MDM example one step further means identifying what information is needed to provide a more complete customer picture, and who are the various people who understand each of the aspects of a customer, for instance, accounts receivable, sales, customer service, etc.
- What technologies are already in place, if any, to support the process? Although data management should be based on addressing business pains, the reality is that a strong IT infrastructure is still required. Organizations cannot overlook the potential need for more servers, better processing speeds, new types of storage and analysis, as well as how this may affect current or future resource requirements.
- How should progress be tracked? There is a constant struggle to identify return on investment (ROI) within data-related projects. What organizations hope to achieve and how this will be measured ends up defining overall success. Developing metrics that include both short-term and longer-term goals can help sell the project to sponsors and ensure proper project management throughout the process.
Fitting all of the pieces together
The development and rollout of a data management program requires many pieces. The questions above provide a glimpse at what’s required to get an initiative off the ground. As organizations become more mature within their analytics applications, the ability to manage that data across the business to provide a consistent and accurate view of what’s happening becomes essential to continued success. This requires looking at how data can be used as a business asset by “connecting the dots” across disparate units to make sure that companies can support their supply chain, partners, and customers on a broader level.
Lyndsay Wise is the president and founder of WiseAnalytics. Lyndsay has ten years of IT experience in business systems analysis, software selection, and implementation of enterprise applications. She provides consulting services for small and mid-sized companies and conducts research into leading technologies, market trends, BI products and vendors, mid-market needs, and data visualization. For more information, visit http://www.wiseanalytics.com.